import os
import json
from PIL import Image
 
# 设置数据集路径
dataset_path = "/workspace/pytorch_performance_metrics/data/coco128"
images_path = os.path.join(dataset_path, "images")
labels_path = os.path.join(dataset_path, "labels")
 
# 类别映射
categories = [
    {'supercategory': 'person', 'id': 1, 'name': 'person'}, {'supercategory': 'vehicle', 'id': 2, 'name': 'bicycle'}, {'supercategory': 'vehicle', 'id': 3, 'name': 'car'}, {'supercategory': 'vehicle', 'id': 4, 'name': 'motorcycle'}, {'supercategory': 'vehicle', 'id': 5, 'name': 'airplane'}, {'supercategory': 'vehicle', 'id': 6, 'name': 'bus'}, {'supercategory': 'vehicle', 'id': 7, 'name': 'train'}, {'supercategory': 'vehicle', 'id': 8, 'name': 'truck'}, {'supercategory': 'vehicle', 'id': 9, 'name': 'boat'}, {'supercategory': 'outdoor', 'id': 10, 'name': 'traffic light'}, {'supercategory': 'outdoor', 'id': 11, 'name': 'fire hydrant'}, {'supercategory': 'outdoor', 'id': 13, 'name': 'stop sign'}, {'supercategory': 'outdoor', 'id': 14, 'name': 'parking meter'}, {'supercategory': 'outdoor', 'id': 15, 'name': 'bench'}, {'supercategory': 'animal', 'id': 16, 'name': 'bird'}, {'supercategory': 'animal', 'id': 17, 'name': 'cat'}, {'supercategory': 'animal', 'id': 18, 'name': 'dog'}, {'supercategory': 'animal', 'id': 19, 'name': 'horse'}, {'supercategory': 'animal', 'id': 20, 'name': 'sheep'}, {'supercategory': 'animal', 'id': 21, 'name': 'cow'}, {'supercategory': 'animal', 'id': 22, 'name': 'elephant'}, {'supercategory': 'animal', 'id': 23, 'name': 'bear'}, {'supercategory': 'animal', 'id': 24, 'name': 'zebra'}, {'supercategory': 'animal', 'id': 25, 'name': 'giraffe'}, {'supercategory': 'accessory', 'id': 27, 'name': 'backpack'}, {'supercategory': 'accessory', 'id': 28, 'name': 'umbrella'}, {'supercategory': 'accessory', 'id': 31, 'name': 'handbag'}, {'supercategory': 'accessory', 'id': 32, 'name': 'tie'}, {'supercategory': 'accessory', 'id': 33, 'name': 'suitcase'}, {'supercategory': 'sports', 'id': 34, 'name': 'frisbee'}, {'supercategory': 'sports', 'id': 35, 'name': 'skis'}, {'supercategory': 'sports', 'id': 36, 'name': 'snowboard'}, {'supercategory': 'sports', 'id': 37, 'name': 'sports ball'}, {'supercategory': 'sports', 'id': 38, 'name': 'kite'}, {'supercategory': 'sports', 'id': 39, 'name': 'baseball bat'}, {'supercategory': 'sports', 'id': 40, 'name': 'baseball glove'}, {'supercategory': 'sports', 'id': 41, 'name': 'skateboard'}, {'supercategory': 'sports', 'id': 42, 'name': 'surfboard'}, {'supercategory': 'sports', 'id': 43, 'name': 'tennis racket'}, {'supercategory': 'kitchen', 'id': 44, 'name': 'bottle'}, {'supercategory': 'kitchen', 'id': 46, 'name': 'wine glass'}, {'supercategory': 'kitchen', 'id': 47, 'name': 'cup'}, {'supercategory': 'kitchen', 'id': 48, 'name': 'fork'}, {'supercategory': 'kitchen', 'id': 49, 'name': 'knife'}, {'supercategory': 'kitchen', 'id': 50, 'name': 'spoon'}, {'supercategory': 'kitchen', 'id': 51, 'name': 'bowl'}, {'supercategory': 'food', 'id': 52, 'name': 'banana'}, {'supercategory': 'food', 'id': 53, 'name': 'apple'}, {'supercategory': 'food', 'id': 54, 'name': 'sandwich'}, {'supercategory': 'food', 'id': 55, 'name': 'orange'}, {'supercategory': 'food', 'id': 56, 'name': 'broccoli'}, {'supercategory': 'food', 'id': 57, 'name': 'carrot'}, {'supercategory': 'food', 'id': 58, 'name': 'hot dog'}, {'supercategory': 'food', 'id': 59, 'name': 'pizza'}, {'supercategory': 'food', 'id': 60, 'name': 'donut'}, {'supercategory': 'food', 'id': 61, 'name': 'cake'}, {'supercategory': 'furniture', 'id': 62, 'name': 'chair'}, {'supercategory': 'furniture', 'id': 63, 'name': 'couch'}, {'supercategory': 'furniture', 'id': 64, 'name': 'potted plant'}, {'supercategory': 'furniture', 'id': 65, 'name': 'bed'}, {'supercategory': 'furniture', 'id': 67, 'name': 'dining table'}, {'supercategory': 'furniture', 'id': 70, 'name': 'toilet'}, {'supercategory': 'electronic', 'id': 72, 'name': 'tv'}, {'supercategory': 'electronic', 'id': 73, 'name': 'laptop'}, {'supercategory': 'electronic', 'id': 74, 'name': 'mouse'}, {'supercategory': 'electronic', 'id': 75, 'name': 'remote'}, {'supercategory': 'electronic', 'id': 76, 'name': 'keyboard'}, {'supercategory': 'electronic', 'id': 77, 'name': 'cell phone'}, {'supercategory': 'appliance', 'id': 78, 'name': 'microwave'}, {'supercategory': 'appliance', 'id': 79, 'name': 'oven'}, {'supercategory': 'appliance', 'id': 80, 'name': 'toaster'}, {'supercategory': 'appliance', 'id': 81, 'name': 'sink'}, {'supercategory': 'appliance', 'id': 82, 'name': 'refrigerator'}, {'supercategory': 'indoor', 'id': 84, 'name': 'book'}, {'supercategory': 'indoor', 'id': 85, 'name': 'clock'}, {'supercategory': 'indoor', 'id': 86, 'name': 'vase'}, {'supercategory': 'indoor', 'id': 87, 'name': 'scissors'}, {'supercategory': 'indoor', 'id': 88, 'name': 'teddy bear'}, {'supercategory': 'indoor', 'id': 89, 'name': 'hair drier'}, {'supercategory': 'indoor', 'id': 90, 'name': 'toothbrush'}
]
 
# YOLO格式转COCO格式的函数
def convert_yolo_to_coco(x_center, y_center, width, height, img_width, img_height):
    x_min = (x_center - width / 2) * img_width
    y_min = (y_center - height / 2) * img_height
    width = width * img_width
    height = height * img_height
    return [round(x_min, 2), round(y_min, 2), round(width, 2), round(height, 2)]
 
# 初始化COCO数据结构
def init_coco_format():
    return {
        "images": [],
        "annotations": [],
        "categories": categories
    }
 
# 处理每个数据集分区
for split in ['train2017']:
    coco_format = init_coco_format()
    annotation_id = 1
 
    for img_name in os.listdir(os.path.join(images_path, split)):
        if img_name.lower().endswith(('.png', '.jpg', '.jpeg')):
            img_path = os.path.join(images_path, split, img_name)
            label_path = os.path.join(labels_path, split, img_name.replace("jpg", "txt"))
 
            img = Image.open(img_path)
            img_width, img_height = img.size
            image_info = {
                "file_name": img_name,
                "id": len(coco_format["images"]) + 1,
                "width": img_width,
                "height": img_height
            }
            coco_format["images"].append(image_info)
 
            if os.path.exists(label_path):
                with open(label_path, "r") as file:
                    for line in file:
                        category_id, x_center, y_center, width, height = map(float, line.split())
                        bbox = convert_yolo_to_coco(x_center, y_center, width, height, img_width, img_height)
                        annotation = {
                            "id": annotation_id,
                            "image_id": image_info["id"],
                            "category_id": int(category_id) + 1,
                            "bbox": bbox,
                            "area": round(bbox[2] * bbox[3], 2),
                            "iscrowd": 0
                        }
                        coco_format["annotations"].append(annotation)
                        annotation_id += 1
 
    # 为每个分区保存JSON文件
    with open(f"{dataset_path}/{split}_coco_format.json", "w") as json_file:
        json.dump(coco_format, json_file, indent=4)